When companies outsource to artificial intelligence (AI), they must ensure that the data they collect is used only for the purpose of creating AI and is kept secure. The Brookings Institution's Artificial Intelligence and Emerging Technology (AIET) Initiative has identified key regulatory and governance issues related to AI and proposed policy solutions to address the complex challenges associated with emerging technologies. Companies must also make sure that the data is easily accessible for their current and future AI projects. Legislative proposals on privacy do not explicitly address AI, so it is important to consider potential concerns regarding AI and privacy, such as discrimination, ethical use, and human control.
Policy options are being discussed to address these issues. To ensure high-quality data sets, it is essential to feed AI models with training data that includes the collective voice rather than a dominant one. As Congress studies comprehensive privacy legislation, it must consider how to address the use of personal information in AI systems. Microsoft, Amazon, and Intel have provided support to The Brookings Institution's AIET Initiative. Companies should assess their access to resources and industry professionals before deciding whether to create their own team or outsource at different stages of their AI project. As AI advances, it can interfere with privacy interests by taking the analysis of personal information to new levels of power and speed.
Companies must be prepared to incorporate AI into their processes with careful preparation or planning. Once the AI model is implemented, companies will begin to obtain real results that demonstrate their efforts were not in vain. Therefore, many companies are struggling to balance competition with the automation and intelligent analysis offered by AI. To ensure data security when outsourcing to AI, companies should take a few steps. First, they should assess their access to resources and industry professionals before deciding whether to create their own team or outsource at different stages of their AI project.
Second, they should consider potential concerns regarding AI and privacy such as discrimination, ethical use, and human control. Third, they should feed AI models with training data that includes the collective voice rather than a dominant one. Finally, they should be prepared to incorporate AI into their processes with careful preparation or planning.
